Two-alternative optimization of moderate batch data processing

Abstract

We consider optimization of moderate batch data processing in the framework of Bernoulli two-armed bandit problem with indefinite control horizon. We assume that there are two alternative processing methods with different a priori unknown efficiencies which are caused by different reasons including those related to legislation. One has to find the most efficient method and to provide its predominant usage. The arising batches of data with close properties have moderate and possibly uncertain sizes. The problem is considered in minimax setting. According to the main theorem of the game theory minimax risk and minimax strategy are searched for as Bayesian ones corresponding to approximately the worst-case prior distribution concentrated on the finite set of parameters. Numerical experiments show that this approach provides good approximations of minimax strategy and minimax risk.